Personalized vasopressor therapy in patients with septic shock

感染性休克患者的个体化升压药治疗

• 批准号: 10506084
• 负责人: SETH BAUER
• 金额: $ 17.63万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10506084
• 关键词: Achievement; Age; Biological; Blood Pressure; Cessation of life; Characteristics; Clinic; Clinical; Clinical Investigator Award; Clinical Pharmacists; Consolidated Framework for Implementation Research; Critical Care; Critical Illness; Data; Data Analytics; Data Science; Data Set; Decision Modeling; Development; Disease; Doctor of Philosophy; Electronic Health Record; Environment; Epinephrine; Ethnography; Failure; Functional disorder; Goals; Health; Health system; Heterogeneity; Hospitals; Hypotension; Individual; Infection; Inflammatory; Infrastructure; Intensive Care Units; International; Intervention; Interview; K-Series Research Career Programs; Knowledge; Leadership; Left Ventricular Ejection Fraction; Life; Logistic Regressions; Machine Learning; Mediating; Mentorship; Methods; Modeling; Modernization; Norepinephrine; Outcome; Output; Patient Care; Patient-Focused Outcomes; Patients; Perception; Pharmaceutical Preparations; Pharmacodynamics; Pharmacology; Positioning Attribute; Prediction of Response to Therapy; Race; Recording of previous events; Regimen; Research; Research Project Grants; Risk; Role; Savings; Scientist; Sepsis; Septic Shock; Shock; Supportive care; Surveys; Techniques; Testing; Training; Translational Research; Translations; Treatment Failure; United States; Vasoconstrictor Agents; Vasopressins; Work; base; behavior change; care delivery; clinical implementation; clinical practice; clinical translation; clinically translatable; cohesion; comorbidity; dosage; implementation framework; implementation science; improved; individual patient; individualized medicine; informatics infrastructure; innovation; model development; mortality; novel; patient response; predictive modeling; pressure; response; risk prediction model; septic patients; sex; sociodemographics; stakeholder perspectives; theories; therapy outcome; tool; treatment effect; treatment research; treatment response; treatment strategy

PROJECT SUMMARY/ABSTRACT Septic shock has a mortality rate above 40%, and is treated with supportive care including vasopressors. Yet, evidence shows vasopressor treatment response and clinician practices are heterogeneous. The key factors mediating this heterogeneity are unknown. This research project investigates the role of patient-specific factors in treatment response to initial and adjunctive vasopressor therapy in patients with septic shock. We seek to develop accurate, individualized tools to predict the heterogeneity of response to a variety of vasopressors in individual septic shock patients and implement these tools into bedside practice. Our preliminary data show early initiation of adjunctive vasopressin is associated with lower mortality, and individual patient and treatment characteristics are associated with vasopressin responsiveness. Therefore, we propose the novel hypothesis that clinically translatable models are capable of predicting early vasopressor treatment response and will optimize initial and adjunctive vasopressor therapy to improve clinical outcomes. This hypothesis will be tested through three interrelated, but independent specific aims: (1) to apply machine learning techniques to predict initial vasopressor therapy response; (2) to develop a decision model for optimizing adjunctive vasopressor therapy outcomes; and (3) to identify determinants of successful vasopressor prediction model clinical translation. Our proposal is innovative in concept because it is the first to use cutting-edge data science methods to develop prediction models for initial and adjunctive vasopressor treatment effects. The proposed research is significant because it has the potential to decrease mortality in septic shock patients by developing and implementing personalized vasopressor treatment approaches. Cleveland Clinic is the ideal environment to complete the proposed research due to the high volume (>6,000 yearly) of clinically and socio- demographically diverse sepsis patients, integrated multi-professional treatment and research teams, extensive informatics infrastructure, large cohesive health-system with a shared electronic health record, and history of successful evidence translation into improved care delivery. As a critical care clinical pharmacist, Dr. Bauer is uniquely positioned to develop and implement innovative vasopressor treatment strategies through knowledge of the combined influences of medication pharmacology and pharmacodynamics, patient comorbidities, and disease pathophysiology on treatment effects. With the mentorship of Dr. Vidula Vachharajani and other internationally-recognized experts, Dr. Bauer will pursue training goals to enhance his understanding of applied clinical translational science, clinical prediction modeling, implementation science, and research leadership. Pursuit of these aims and training goals through the K08 Career Development Award mechanism will facilitate achievement of Dr. Bauer’s long-term goal of becoming an independent clinician-scientist who improves the health and outcomes of patients with septic shock through the optimized utilization of vasopressor therapies.

项目摘要/摘要 感染性休克的死亡率超过40%，并接受包括血管加压药在内的支持性护理。然而， 有证据表明，血管加压剂的治疗反应和临床实践是不同的。关键因素 调节这种异质性的原因尚不清楚。这项研究项目调查了患者特定因素的作用。 感染性休克患者对初始和辅助加压药治疗的反应。我们寻求 开发准确、个性化的工具来预测对各种血管加压剂的反应的异质性 对感染性休克患者个体化，并将这些工具用于床边实践。我们的初步数据显示 辅助性加压素的启动与较低的死亡率、个别患者和治疗有关 这些特征与加压素的反应性有关。因此，我们提出了新的假设。 临床上可翻译的模型能够预测早期血管加压剂治疗反应和将 优化初始和辅助血管加压治疗，以改善临床结果。这一假设将得到检验。 通过三个相互关联但独立的具体目标：(1)应用机器学习技术来预测 初始血管加压药治疗反应；(2)开发优化辅助药物的决策模型 血管加压药治疗结果；以及(3)确定成功预测加压药的决定因素。 模范临床翻译。我们的方案在概念上是创新的，因为它是第一个使用尖端数据的方案 科学方法开发初始和辅助血管加压药治疗效果的预测模型。这个 拟议的研究具有重要意义，因为它有可能通过以下方式降低感染性休克患者的死亡率 开发和实施个性化的血管加压剂治疗方法。克利夫兰诊所是理想的选择 由于临床和社会研究的大量(每年6,000英镑)，完成拟议研究的环境 人口统计多样化的脓毒症患者，整合的多专业治疗和研究团队，广泛 信息学基础设施、具有共享电子健康记录的大型内聚健康系统以及 成功地将证据转化为更好的护理服务。作为一名重症监护临床药剂师，鲍尔博士 通过知识开发和实施创新的血管升压药治疗策略的独特地位 药物药理学和药效学、患者合并症和 疾病病理生理学对治疗效果的影响。在Vidula Vachharajani博士和其他人的指导下 国际公认的专家，鲍尔博士将追求培训目标，以增强他对应用程序的理解 临床翻译科学、临床预测建模、实施科学和研究领导。 通过K08职业发展奖励机制追求这些目标和培训目标将有助于 鲍尔博士成为一名独立的临床医生-科学家的长期目标的实现 感染性休克患者的健康和预后通过血管加压药治疗的优化利用。